This week, articles are still awash with 2020 predictions, to save you from reading yet another future outlook article, here’s a few of the other things we’ve been reading this week.
Deloitte Talks Data Ethics
The social intelligence industry is still in need of a universal set of data ethics, we all know they are much needed (this is something we’re looking to tackle later this year, stay tuned for updates). In the meantime, here is what Deloitte has to say about data ethics in general.
Debate: Social Intelligence Should be Adopted by All Insights Departments
We’re starting a debate on the future of social intelligence in consumer insights, our first debate explores ‘Social media intelligence should be a research method adopted by all insights functions, discuss…’, have your say now.
New Academic Paper: Big Data Analytics: Challenges and Applications for Text, Audio, and Social Media Data
New peer-reviewed research coming from the International Journal of Soft Computing, Artificial Intelligence, and Applications (IJSCAI).
All types of machine automated systems are generating large amounts of data in different forms like statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper, we are discussing issues, challenges, and application of these types of Big Data with the consideration of big data dimensions. Here we are discussing social media data analytics, content-based analytics, text data analytics, audio, and video data analytics their issues and expected application areas. It will motivate researchers to address these issues of storage, management, and retrieval of data known as Big Data. As well as the usages of Big Data analytics in India is also highlighted.
Viewpoint: The Reuse of Social Data Without Consent
Many of you might have missed this tweet from a #LAC2020 – what’s your thoughts on this viewpoint?
Marissa Takahashi relates the problem of reuse of social media data without consent back to the story of the use of Henrietta Lacks’ cells for extensive research and commercialisation without consent. This is not a new problem. #LCA2020 pic.twitter.com/qKI9Rf9kn3
— Betsy (@betsybookworm) January 16, 2020
An Effective Way to Identify Online Trolling
Researchers at California Institute of Technology believe that they have demonstrated that machine-learning algorithms can monitor online social media conversations as they evolve, which could one day lead to an effective and automated way to spot online trolling.
Viewpoint: The Problems with Social-Political Polling
We thought this Twitter exchange was interesting. A new report published by The Social Media Insights Lab at The University of South Carolina published research exploring the online conversations regarding democratic candidates. This was then interpreted and published by a reporter as highlighting the social frontrunner, suggesting his a contender to win. However, the significance of this finding was then questioned by another Twitter user.
This is a good example of not letting the data be interpreted out of context, and the problems with laypeople interpreting insights from social data.
.@UofSCInsights is out with a new report that shows “@PeteButtigieg has emerged in South Carolina social media conversations as the most significant threat to Democratic frontrunner @JoeBiden, according to a new analysis by @UofSCInsights” #SC2020 https://t.co/s3DKK0JPiF pic.twitter.com/HFXtTBPBUj
— Gavin Jackson (@GavinJackson) January 15, 2020
Hi @HenriThompsonSC, this is only an analysis of social media data of South Carolina social media users – mainly coming from Twitter. Therefore, it does not capture every person in the Palmetto State. It is not a poll, but serves to capture where digital “buzz” lies.
— UofSCInsights (@UofSCInsights) January 15, 2020
Father Says Social Media Firms Should be Forced to Hand Over Data
The father of a girl who took her own life has blamed social media for helping to kill his daughter – as he backed a report calling for companies to share data and pay towards research into the technology’s potential harms.
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